I. Introduction
In recent years, the growing interest in developing Question Answering (QA) systems, driven by their potential to enhance information retrieval and user interaction, especially within the domain of Natural Language Processing (NLP), has become integral [1]. This research focuses on advancing QA systems, specifically within the complex realm of public transportation. The public transportation network in Bandung, Indonesia, com-prises minibuses (Angkot), Trans Metro Bandung (TMB) buses, School Buses, and the unique Teman Bus program [2]. Given the intricacies of this multifaceted system, commuters face challenges in obtaining accurate and timely information, neces-sitating the development of an intelligent QA system tailored to the diverse modes of public transportation in Bandung.